{"id":"https://openalex.org/W2984580555","doi":"https://doi.org/10.1145/3347146.3359089","title":"Semantic-Enhanced Learning (SEL) on Artificial Neural Networks Using the Example of Semantic Location Prediction","display_name":"Semantic-Enhanced Learning (SEL) on Artificial Neural Networks Using the Example of Semantic Location Prediction","publication_year":2019,"publication_date":"2019-11-05","ids":{"openalex":"https://openalex.org/W2984580555","doi":"https://doi.org/10.1145/3347146.3359089","mag":"2984580555"},"language":"en","primary_location":{"id":"doi:10.1145/3347146.3359089","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3347146.3359089","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5004407743","display_name":"Antonios Karatzoglou","orcid":"https://orcid.org/0000-0002-7939-1408"},"institutions":[{"id":"https://openalex.org/I889804353","display_name":"Robert Bosch (Germany)","ror":"https://ror.org/01fe0jt45","country_code":"DE","type":"company","lineage":["https://openalex.org/I889804353"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Antonios Karatzoglou","raw_affiliation_strings":["Robert Bosch GmbH, Stuttgart, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Robert Bosch GmbH, Stuttgart, Germany","institution_ids":["https://openalex.org/I889804353"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082553444","display_name":"Michael Beigl","orcid":"https://orcid.org/0000-0001-5009-2327"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Michael Beigl","raw_affiliation_strings":["Karlsruhe Institute of Technology, Karlsruhe, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology, Karlsruhe, Germany","institution_ids":["https://openalex.org/I102335020"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.4506,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.85897331,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"448","last_page":"451"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9905999898910522,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9902999997138977,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8015565276145935},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7368187308311462},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.625649631023407},{"id":"https://openalex.org/keywords/semantic-computing","display_name":"Semantic computing","score":0.5144234299659729},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4530758559703827},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35238850116729736},{"id":"https://openalex.org/keywords/semantic-web","display_name":"Semantic Web","score":0.2305348515510559}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8015565276145935},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7368187308311462},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.625649631023407},{"id":"https://openalex.org/C511149849","wikidata":"https://www.wikidata.org/wiki/Q7449051","display_name":"Semantic computing","level":3,"score":0.5144234299659729},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4530758559703827},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35238850116729736},{"id":"https://openalex.org/C2129575","wikidata":"https://www.wikidata.org/wiki/Q54837","display_name":"Semantic Web","level":2,"score":0.2305348515510559}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3347146.3359089","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3347146.3359089","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.47999998927116394,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1647729745","https://openalex.org/W1663973292","https://openalex.org/W2050466268","https://openalex.org/W2086961842","https://openalex.org/W2087739686","https://openalex.org/W2115733720","https://openalex.org/W2143554828","https://openalex.org/W2186046013","https://openalex.org/W2585377437","https://openalex.org/W2766738555","https://openalex.org/W2893116530","https://openalex.org/W2896248194","https://openalex.org/W2900766759","https://openalex.org/W2946760638","https://openalex.org/W2951798058"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347","https://openalex.org/W4210805261"],"abstract_inverted_index":{"Recent":[0],"machine":[1],"learning":[2,95],"models":[3],"find":[4],"a":[5,30,36,45,88,117,192],"widespread":[6],"use":[7],"whether":[8],"in":[9,17,85,162,191],"respect":[10],"of":[11,39,77,94,98,157,183],"data":[12,80],"mining":[13],"and":[14,34,55,81,100,131,166,186],"forecasting":[15],"or":[16],"the":[18,64,143,154,158,176],"classification":[19],"domain.":[20],"However,":[21],"real-world":[22,200],"situations":[23],"comprise":[24],"complex":[25],"estimation":[26],"tasks":[27],"that":[28,110],"carry":[29],"certain":[31,37],"semantic":[32,113,140,155,193],"load":[33],"bring":[35],"degree":[38],"fuzziness":[40,46],"with":[41,125,151],"them.":[42],"This":[43],"is":[44,147],"which":[47],"humans,":[48],"due":[49],"to":[50,91,102,121,127,142,148],"their":[51,56,189],"common":[52],"sense":[53],"knowledge":[54,114],"personal":[57],"experience,":[58],"can":[59],"easily":[60],"understand":[61],"by":[62],"linking":[63],"underlying":[65],"concepts":[66],"together,":[67],"while":[68,171],"machines":[69],"may":[70],"from":[71],"scratch":[72],"not.":[73],"A":[74],"vast":[75],"amount":[76],"both":[78],"training":[79,128,169],"time":[82,170],"are":[83],"necessary":[84],"order":[86],"for":[87],"computational":[89],"model":[90,119],"be":[92],"capable":[93],"such":[96],"kind":[97],"relations":[99],"adapting":[101],"new":[103],"situations.":[104],"In":[105,133],"this":[106,163],"work,":[107],"we":[108,135,187],"show":[109],"letting":[111],"explicit":[112],"flow":[115],"into":[116],"predictive":[118],"leads":[120],"an":[122,138],"improved":[123],"performance":[124],"regard":[126],"time,":[129],"accuracy":[130],"robustness.":[132],"particular,":[134],"propose":[136],"adding":[137],"auxiliary":[139],"layer":[141],"model,":[144],"whose":[145],"role":[146],"provide":[149],"it":[150],"information":[152],"about":[153],"interrelation":[156],"treated":[159],"classes":[160],"creating":[161],"way":[164],"shortcuts":[165],"saving":[167],"valuable":[168],"improving":[172],"its":[173],"quality":[174],"at":[175],"same":[177],"time.":[178],"We":[179],"explore":[180],"several":[181],"versions":[182],"our":[184],"approach":[185],"illustrate":[188],"functionality":[190],"location":[194],"prediction":[195],"scenario":[196],"using":[197],"2":[198],"different":[199],"datasets.":[201]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
